AI-Driven Smart Logistics Route Optimization Using IoT and Machine Learning for Efficient Supply Chain Management

IoT and Machine Learning-Driven Smart Logistics Route Optimization improves supply chain efficiency by utilizing real-time data and predictive analytics to reduce delays, streamline routes, and enhance transportation performance. Supply Chain Management optimizes resources and processes to streamline the flow of goods, services, and information. Maintaining high-quality real-time data and handling the computational demands of AI models pose significant challenges in AI-driven logistics route optimization, impacting accuracy, scalability, and timely decision-making. Reinforcement Learning (RL), leveraging Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), optimizes logistics routes by dynamically reducing waiting time, accounting for traffic conditions, and minimizing delays to enhance efficiency. Deep Q-Networks and Proximal Policy Optimization improve logistics route optimization by facilitating real-time adaptive decision-making in dynamic traffic conditions, reducing delays, and maximizing resource efficiency. The PPO model surpasses the DQN model with higher accuracy of 0.91 compared to 0.5154, precision of 1.00 against 0.5161, recall of 0.81 versus 0.1441, and an F1 score of 0.89 over 0.2254, proving its effectiveness in logistics route optimization. Furthermore, with a low time complexity of 0.4663 seconds, the PPO model ensures efficient real-time decision-making.